Internet Infrastructures for Big Data
Talk given at Verisign's Distinguished Speaker Series, 2014
Prof. Philippe Cudre-Mauroux
eXascale Infolab
http://exascale.info/
Big data is used to describe a massive volume of both structured and unstructured data that is so large that it's difficult to process using traditional database and software techniques. In most enterprise scenarios the data is too big or it moves too fast or it exceeds current processing capacity. The term big data is believed to have originated with Web search companies who had to query very large distributed aggregations of loosely-structured data.
Big Stream Processing Systems, Big GraphsPetr Novotný
Big Data, a recent phenomenon. Everyone talks about it, but do you really know what Big Data is? Join our four-part series about Big Data and you will get answers to your questions!
We will cover Introduction to Big Data and available platforms which we can use to deal with Big Data. And in the end, we are going to give you an insight into the possible future of dealing with Big Data.
After the two previous episodes you know the basics about Big Data. Yet, it might get a bit more complicated than that. Usually when you have to deal with data which is generated in real-time. In this case, you are dealing with Big Stream.
This episode of our series will be focussed on processing systems capable of dealing with Big Streams. But analysing data lacking graphical representation will not be very convenient for us. And this is where we have to use a platform capable of visualising Big Graphs. All these topics will be covered in today’s presentation.
#CHEDTEB
www.chedteb.eu
Big data refers to large, complex datasets that are difficult to process using traditional database management tools. There are four key characteristics of big data: volume, velocity, variety, and veracity. Various sources generate big data, including social media, scientific instruments, mobile devices, sensors, and more. Analyzing big data can provide benefits like cost reductions, time reductions, new product development, and smarter business decisions. Hadoop Distributed File System (HDFS) and Hadoop software platform provide scalable and cost-effective infrastructure for storing and processing big data across commodity servers in a cluster.
This document discusses big data, defining it as data that is too large and complex for traditional data processing systems due to its volume, variety and velocity. It outlines the 3Vs of big data - volume, referring to the large amount of data being generated daily; variety, referring to different data formats; and velocity, referring to the speed at which data is generated and needs to be processed. The document also discusses characteristics of big data like structured, semi-structured and unstructured data, benefits of big data, challenges of capturing, storing, analyzing and presenting big data, and technologies like Hadoop and MapReduce used for big data solutions.
Many believe Big Data is a brand new phenomenon. It isn't, it is part of an evolution that reaches far back history. Here are some of the key milestones in this development.
Introduction to Cloud Computing and Big Datawaheed751
This document provides an introduction to cloud computing and big data. It defines cloud computing as a model for providing scalable computing resources over the internet with minimal management. The key characteristics of cloud computing include on-demand self-service, broad network access, resource pooling, rapid elasticity, and measured service. Dynamic provisioning allows cloud resources to scale up and down based on demand. This helps solve the problems of underutilization and overload in traditional systems with static capacity. The document also discusses how dynamic provisioning can be used in multi-tier web applications running on cloud infrastructure.
Big data is used to describe a massive volume of both structured and unstructured data that is so large that it's difficult to process using traditional database and software techniques. In most enterprise scenarios the data is too big or it moves too fast or it exceeds current processing capacity. The term big data is believed to have originated with Web search companies who had to query very large distributed aggregations of loosely-structured data.
Big Stream Processing Systems, Big GraphsPetr Novotný
Big Data, a recent phenomenon. Everyone talks about it, but do you really know what Big Data is? Join our four-part series about Big Data and you will get answers to your questions!
We will cover Introduction to Big Data and available platforms which we can use to deal with Big Data. And in the end, we are going to give you an insight into the possible future of dealing with Big Data.
After the two previous episodes you know the basics about Big Data. Yet, it might get a bit more complicated than that. Usually when you have to deal with data which is generated in real-time. In this case, you are dealing with Big Stream.
This episode of our series will be focussed on processing systems capable of dealing with Big Streams. But analysing data lacking graphical representation will not be very convenient for us. And this is where we have to use a platform capable of visualising Big Graphs. All these topics will be covered in today’s presentation.
#CHEDTEB
www.chedteb.eu
Big data refers to large, complex datasets that are difficult to process using traditional database management tools. There are four key characteristics of big data: volume, velocity, variety, and veracity. Various sources generate big data, including social media, scientific instruments, mobile devices, sensors, and more. Analyzing big data can provide benefits like cost reductions, time reductions, new product development, and smarter business decisions. Hadoop Distributed File System (HDFS) and Hadoop software platform provide scalable and cost-effective infrastructure for storing and processing big data across commodity servers in a cluster.
This document discusses big data, defining it as data that is too large and complex for traditional data processing systems due to its volume, variety and velocity. It outlines the 3Vs of big data - volume, referring to the large amount of data being generated daily; variety, referring to different data formats; and velocity, referring to the speed at which data is generated and needs to be processed. The document also discusses characteristics of big data like structured, semi-structured and unstructured data, benefits of big data, challenges of capturing, storing, analyzing and presenting big data, and technologies like Hadoop and MapReduce used for big data solutions.
Many believe Big Data is a brand new phenomenon. It isn't, it is part of an evolution that reaches far back history. Here are some of the key milestones in this development.
Introduction to Cloud Computing and Big Datawaheed751
This document provides an introduction to cloud computing and big data. It defines cloud computing as a model for providing scalable computing resources over the internet with minimal management. The key characteristics of cloud computing include on-demand self-service, broad network access, resource pooling, rapid elasticity, and measured service. Dynamic provisioning allows cloud resources to scale up and down based on demand. This helps solve the problems of underutilization and overload in traditional systems with static capacity. The document also discusses how dynamic provisioning can be used in multi-tier web applications running on cloud infrastructure.
Keynote Address at 2013 CloudCon: Future of Big Data by Richard McDougall (In...exponential-inc
Big data workloads are growing rapidly and require unprecedented scale in data storage, processing power and bandwidth. Cloud infrastructure provides the flexibility and economics needed to support big data's mixed workloads through software-defined compute, storage, networking and security. This allows organizations to gain real-time insights from massive data sets and harness big data's potential for industries like retail, manufacturing and more.
This document defines big data and discusses techniques for integrating large and complex datasets. It describes big data as collections that are too large for traditional database tools to handle. It outlines the "3Vs" of big data: volume, velocity, and variety. It also discusses challenges like heterogeneous structures, dynamic and continuous changes to data sources. The document summarizes techniques for big data integration including schema mapping, record linkage, data fusion, MapReduce, and adaptive blocking that help address these challenges at scale.
This document summarizes the history of big data from 1944 to 2013. It outlines key milestones such as the first use of the term "big data" in 1997, the growth of internet traffic in the late 1990s, Doug Laney coining the three V's of big data in 2001, and the focus of big data professionals shifting from IT to business functions that utilize data in 2013. The document serves to illustrate how data storage and analysis have evolved over time due to technological advances and changing needs.
This document discusses big data, including its definition as large volumes of structured and unstructured data from various sources that represents an ongoing source for discovery and analysis. It describes the 3 V's of big data - volume, velocity and variety. Volume refers to the large amount of data stored, velocity is the speed at which the data is generated and processed, and variety means the different data formats. The document also outlines some advantages and disadvantages of big data, challenges in capturing, storing, sharing and analyzing large datasets, and examples of big data applications.
The New Convergence of Data; the Next Strategic Business AdvantageJoAnna Cheshire
The document discusses the new convergence of data and how it is becoming a critical strategic business asset. Some key points:
- Data is growing exponentially in terms of volume, variety and velocity. Variety, not just volume, will drive new investments.
- Data is now a primary business asset and new business processes will revolve around data. Data science is becoming a key way for organizations to gain competitive advantages.
- Emerging technologies like the Internet of Things, artificial intelligence, cloud computing and more are fueling the growth of data. This will create both opportunities and challenges for organizations to harness data effectively.
This document provides an overview of big data, including its definition, characteristics, sources, tools, applications, risks and benefits. It defines big data as large volumes of diverse data that can be analyzed to reveal patterns and trends. The three key characteristics are volume, velocity and variety. Examples of big data sources include social media, sensors and user data. Tools used for big data include Hadoop, MongoDB and analytics programs. Big data has many applications and benefits but also risks regarding privacy and regulation. The future of big data is strong with the market expected to grow significantly in coming years.
This document discusses data mining with big data. It defines data mining as the process of discovering patterns in large data sets and big data as collections of data that are too large to process using traditional software tools. The document notes that 2.5 quintillion bytes of data are created daily and that 90% of data was produced in the past two years. It provides examples of big data like presidential debates and photos. It also discusses challenges of mining big data due to its huge volume and complex, evolving relationships between data points.
It describe cloud infrastructure required for big data. It discusses the object storage and virtualization required for big data. Ceph is discussed as example.
Big Data is a new term used to identify datasets that we can not manage with current methodologies or data mining software tools due to their large size and complexity. Big Data mining is the capability of extracting useful information from these large datasets or streams of data. New mining techniques are necessary due to the volume, variability, and velocity, of such data.
This document discusses data mining on big data. It begins with an introduction to big data and data mining. It then discusses the characteristics of big data, known as the HASE theorem, which are that big data is huge in volume, heterogeneous and diverse, from autonomous sources, and has complex and evolving relationships. It presents a conceptual framework for big data processing with three tiers: tier I focuses on low-level data access and computing using techniques like MapReduce; tier II concentrates on semantics, knowledge and privacy; and tier III addresses data mining algorithms. The document concludes that high performance computing platforms are needed to fully leverage big data.
Big data comes from a variety of sources such as sensors, social media, digital pictures, purchase transactions, and cell phone GPS signals. The volume of data created each day is vast, with 2.5 quintillion bytes created daily, 90% of which has been created in just the last two years. Big data is characterized by its volume, variety, velocity and value. It requires new tools like Hadoop and MapReduce to store and analyze data across distributed systems. When dealing with big data, once complex modeling can sometimes be replaced by simple counting techniques due to the large amount of data available. Companies are beginning to generate value from big data through new insights and business models.
This document discusses big data and data mining. It defines big data as large volumes of structured and unstructured data that are difficult to process using traditional techniques due to their size. It outlines the 4 Vs of big data: volume, velocity, variety, and veracity. The proposed system would use distributed parallel computing with Hadoop to identify relationships in huge amounts of data from different sources and dimensions. It discusses challenges of big data like data location, volume, privacy, and gaining insights. Solutions involve parallel programming, distributed storage, and access restrictions.
How to Create the Google for Earth Data (XLDB 2015, Stanford)Rainer Sternfeld
Rainer Sternfeld presented on creating a Google-like platform for earth data using Planet OS. He described the challenges NOAA faces in managing tens of terabytes of weather data per day across scattered systems. Planet OS could index NOAA's metadata and downsample remote datasets via APIs. It would store chunked array data in object stores like S3 and provide on-demand computing via cloud services. This would make NOAA's large-scale data easily discoverable and machine-readable while addressing issues like data volume, transport, and real-time dissemination.
This document discusses data mining with big data. It defines big data and data mining. Big data is characterized by its volume, variety, and velocity. The amount of data in the world is growing exponentially with 2.5 quintillion bytes created daily. The proposed system would use distributed parallel computing with Hadoop to handle large volumes of varied data types. It would provide a platform to process data across dimensions and summarize results while addressing challenges such as data location, privacy, and hardware resources.
This document provides an overview of big data. It defines big data as large volumes of data that come in many varieties and are created quickly. It notes that while 5 billion gigabytes of data were created from the beginning of recorded time until 2003, that same amount was being created every two days by 2011 and every 10 minutes by 2013 due to the growth of data from sources like the internet and smartphones. The document also outlines some of the common tools used to analyze big data, potential benefits and challenges, as well as which industries are most impacted.
Big Data Characteristics And Process PowerPoint Presentation SlidesSlideTeam
We present you content-ready big data characteristics and process PowerPoint presentation that can be used to present content management techniques. It can be presented by IT consulting and analytics firms to their clients or company’s management. This relational database management PPT design comprises of 53 slides including introduction, facts, how big is big data, market forecast, sources, 3Vs and 5Vs small Vs big data, objective, technologies, workflow, four phases, types, information analytics process, impact, benefits, future, opportunities and challenges etc. Our data transformation PowerPoint templates are apt to present various topics such as information management concepts and technologies, transforming facts with intelligence, data analysis framework, data mining, technology platforms, data transfer and visualization, content management, Internet of things, data storage and analysis, information infrastructure, datasets, technology and cloud computing. Download big data characteristics and process PPT graphics to make an impressive presentation. Develop greater goodwill with our Big Data Characteristics And Process PowerPoint Presentation Slides. Folks feel friendlier towards you.
The document discusses the importance of data for evidence-based policymaking, organizational development, detecting security issues, and improving business outcomes. It provides examples of how New Zealand Registry Services (NZRS) uses data for these purposes, including operating a national broadband map and open data portals. The document advocates for making more data openly available to enable reproducible research, more informed policy debates, and increased public trust.
- Big data refers to large volumes of data from various sources that is analyzed to reveal patterns, trends, and associations.
- The evolution of big data has seen it grow from just volume, velocity, and variety to also include veracity, variability, visualization, and value.
- Analyzing big data can provide hidden insights and competitive advantages for businesses by finding trends and patterns in large amounts of structured and unstructured data from multiple sources.
A l'occasion de l'eGov Innovation Day 2014 - DONNÉES DE L’ADMINISTRATION, UNE MINE (qui) D’OR(t) - Philippe Cudré-Mauroux présente Big Data et eGovernment.
This document provides an introduction to big data, including definitions and key concepts. It discusses the evolution of computing systems and data storage. Big data is defined as large and complex data sets that are difficult to process using traditional methods due to the volume, variety, velocity, and veracity of the data. Examples of big data sources and applications are provided. Finally, different approaches for analyzing big data are described, including MapReduce, Hadoop, real-time analytics using databases, and complex event processing.
Keynote Address at 2013 CloudCon: Future of Big Data by Richard McDougall (In...exponential-inc
Big data workloads are growing rapidly and require unprecedented scale in data storage, processing power and bandwidth. Cloud infrastructure provides the flexibility and economics needed to support big data's mixed workloads through software-defined compute, storage, networking and security. This allows organizations to gain real-time insights from massive data sets and harness big data's potential for industries like retail, manufacturing and more.
This document defines big data and discusses techniques for integrating large and complex datasets. It describes big data as collections that are too large for traditional database tools to handle. It outlines the "3Vs" of big data: volume, velocity, and variety. It also discusses challenges like heterogeneous structures, dynamic and continuous changes to data sources. The document summarizes techniques for big data integration including schema mapping, record linkage, data fusion, MapReduce, and adaptive blocking that help address these challenges at scale.
This document summarizes the history of big data from 1944 to 2013. It outlines key milestones such as the first use of the term "big data" in 1997, the growth of internet traffic in the late 1990s, Doug Laney coining the three V's of big data in 2001, and the focus of big data professionals shifting from IT to business functions that utilize data in 2013. The document serves to illustrate how data storage and analysis have evolved over time due to technological advances and changing needs.
This document discusses big data, including its definition as large volumes of structured and unstructured data from various sources that represents an ongoing source for discovery and analysis. It describes the 3 V's of big data - volume, velocity and variety. Volume refers to the large amount of data stored, velocity is the speed at which the data is generated and processed, and variety means the different data formats. The document also outlines some advantages and disadvantages of big data, challenges in capturing, storing, sharing and analyzing large datasets, and examples of big data applications.
The New Convergence of Data; the Next Strategic Business AdvantageJoAnna Cheshire
The document discusses the new convergence of data and how it is becoming a critical strategic business asset. Some key points:
- Data is growing exponentially in terms of volume, variety and velocity. Variety, not just volume, will drive new investments.
- Data is now a primary business asset and new business processes will revolve around data. Data science is becoming a key way for organizations to gain competitive advantages.
- Emerging technologies like the Internet of Things, artificial intelligence, cloud computing and more are fueling the growth of data. This will create both opportunities and challenges for organizations to harness data effectively.
This document provides an overview of big data, including its definition, characteristics, sources, tools, applications, risks and benefits. It defines big data as large volumes of diverse data that can be analyzed to reveal patterns and trends. The three key characteristics are volume, velocity and variety. Examples of big data sources include social media, sensors and user data. Tools used for big data include Hadoop, MongoDB and analytics programs. Big data has many applications and benefits but also risks regarding privacy and regulation. The future of big data is strong with the market expected to grow significantly in coming years.
This document discusses data mining with big data. It defines data mining as the process of discovering patterns in large data sets and big data as collections of data that are too large to process using traditional software tools. The document notes that 2.5 quintillion bytes of data are created daily and that 90% of data was produced in the past two years. It provides examples of big data like presidential debates and photos. It also discusses challenges of mining big data due to its huge volume and complex, evolving relationships between data points.
It describe cloud infrastructure required for big data. It discusses the object storage and virtualization required for big data. Ceph is discussed as example.
Big Data is a new term used to identify datasets that we can not manage with current methodologies or data mining software tools due to their large size and complexity. Big Data mining is the capability of extracting useful information from these large datasets or streams of data. New mining techniques are necessary due to the volume, variability, and velocity, of such data.
This document discusses data mining on big data. It begins with an introduction to big data and data mining. It then discusses the characteristics of big data, known as the HASE theorem, which are that big data is huge in volume, heterogeneous and diverse, from autonomous sources, and has complex and evolving relationships. It presents a conceptual framework for big data processing with three tiers: tier I focuses on low-level data access and computing using techniques like MapReduce; tier II concentrates on semantics, knowledge and privacy; and tier III addresses data mining algorithms. The document concludes that high performance computing platforms are needed to fully leverage big data.
Big data comes from a variety of sources such as sensors, social media, digital pictures, purchase transactions, and cell phone GPS signals. The volume of data created each day is vast, with 2.5 quintillion bytes created daily, 90% of which has been created in just the last two years. Big data is characterized by its volume, variety, velocity and value. It requires new tools like Hadoop and MapReduce to store and analyze data across distributed systems. When dealing with big data, once complex modeling can sometimes be replaced by simple counting techniques due to the large amount of data available. Companies are beginning to generate value from big data through new insights and business models.
This document discusses big data and data mining. It defines big data as large volumes of structured and unstructured data that are difficult to process using traditional techniques due to their size. It outlines the 4 Vs of big data: volume, velocity, variety, and veracity. The proposed system would use distributed parallel computing with Hadoop to identify relationships in huge amounts of data from different sources and dimensions. It discusses challenges of big data like data location, volume, privacy, and gaining insights. Solutions involve parallel programming, distributed storage, and access restrictions.
How to Create the Google for Earth Data (XLDB 2015, Stanford)Rainer Sternfeld
Rainer Sternfeld presented on creating a Google-like platform for earth data using Planet OS. He described the challenges NOAA faces in managing tens of terabytes of weather data per day across scattered systems. Planet OS could index NOAA's metadata and downsample remote datasets via APIs. It would store chunked array data in object stores like S3 and provide on-demand computing via cloud services. This would make NOAA's large-scale data easily discoverable and machine-readable while addressing issues like data volume, transport, and real-time dissemination.
This document discusses data mining with big data. It defines big data and data mining. Big data is characterized by its volume, variety, and velocity. The amount of data in the world is growing exponentially with 2.5 quintillion bytes created daily. The proposed system would use distributed parallel computing with Hadoop to handle large volumes of varied data types. It would provide a platform to process data across dimensions and summarize results while addressing challenges such as data location, privacy, and hardware resources.
This document provides an overview of big data. It defines big data as large volumes of data that come in many varieties and are created quickly. It notes that while 5 billion gigabytes of data were created from the beginning of recorded time until 2003, that same amount was being created every two days by 2011 and every 10 minutes by 2013 due to the growth of data from sources like the internet and smartphones. The document also outlines some of the common tools used to analyze big data, potential benefits and challenges, as well as which industries are most impacted.
Big Data Characteristics And Process PowerPoint Presentation SlidesSlideTeam
We present you content-ready big data characteristics and process PowerPoint presentation that can be used to present content management techniques. It can be presented by IT consulting and analytics firms to their clients or company’s management. This relational database management PPT design comprises of 53 slides including introduction, facts, how big is big data, market forecast, sources, 3Vs and 5Vs small Vs big data, objective, technologies, workflow, four phases, types, information analytics process, impact, benefits, future, opportunities and challenges etc. Our data transformation PowerPoint templates are apt to present various topics such as information management concepts and technologies, transforming facts with intelligence, data analysis framework, data mining, technology platforms, data transfer and visualization, content management, Internet of things, data storage and analysis, information infrastructure, datasets, technology and cloud computing. Download big data characteristics and process PPT graphics to make an impressive presentation. Develop greater goodwill with our Big Data Characteristics And Process PowerPoint Presentation Slides. Folks feel friendlier towards you.
The document discusses the importance of data for evidence-based policymaking, organizational development, detecting security issues, and improving business outcomes. It provides examples of how New Zealand Registry Services (NZRS) uses data for these purposes, including operating a national broadband map and open data portals. The document advocates for making more data openly available to enable reproducible research, more informed policy debates, and increased public trust.
- Big data refers to large volumes of data from various sources that is analyzed to reveal patterns, trends, and associations.
- The evolution of big data has seen it grow from just volume, velocity, and variety to also include veracity, variability, visualization, and value.
- Analyzing big data can provide hidden insights and competitive advantages for businesses by finding trends and patterns in large amounts of structured and unstructured data from multiple sources.
A l'occasion de l'eGov Innovation Day 2014 - DONNÉES DE L’ADMINISTRATION, UNE MINE (qui) D’OR(t) - Philippe Cudré-Mauroux présente Big Data et eGovernment.
This document provides an introduction to big data, including definitions and key concepts. It discusses the evolution of computing systems and data storage. Big data is defined as large and complex data sets that are difficult to process using traditional methods due to the volume, variety, velocity, and veracity of the data. Examples of big data sources and applications are provided. Finally, different approaches for analyzing big data are described, including MapReduce, Hadoop, real-time analytics using databases, and complex event processing.
1) Big data is being generated from many sources like web data, e-commerce purchases, banking transactions, social networks, science experiments, and more. The volume of data is huge and growing exponentially.
2) Big data is characterized by its volume, velocity, variety, and value. It requires new technologies and techniques for capture, storage, analysis, and visualization.
3) Analyzing big data can provide valuable insights but also poses challenges related to cost, integration of diverse data types, and shortage of data science experts. New platforms and tools are being developed to make big data more accessible and useful.
Geospatial Intelligence Middle East 2013_Big Data_Steven RamageSteven Ramage
Some initial considerations and discussion points around geospatial big data. Location adds context and relevance. Need to consider a number of V factors including Value.
This document provides an introduction to big data. It defines big data as large and complex data sets that are difficult to process using traditional data management tools. It discusses the three V's of big data - volume, variety and velocity. Volume refers to the large scale of data. Variety means different data types. Velocity means the speed at which data is generated and processed. The document outlines topics that will be covered, including Hadoop, MapReduce, data mining techniques and graph databases. It provides examples of big data sources and challenges in capturing, analyzing and visualizing large and diverse data sets.
This document provides an introduction to a training course on big data analytics. It discusses why big data has become important due to the exponential growth in data volume, velocity, and variety. The course aims to focus on cloud-based storage and processing of big data using systems like HDFS, MapReduce, HBase and Storm. It emphasizes that learning involves actively asking questions. Big data is introduced by explaining the three V's of volume, velocity and variety. Examples of big data usage are given in areas like baseball analytics, political campaigns and election predictions. Challenges of big data integration and processing large volumes of heterogeneous data are also covered.
Big Data brings big promise and also big challenges, the primary and most important one being the ability to deliver Value to business stakeholders who are not data scientists!
DevOps for Data Engineers - Automate Your Data Science Pipeline with Ansible,...Mihai Criveti
- The document discusses automating data science pipelines with DevOps tools like Ansible, Packer, and Kubernetes.
- It covers obtaining data, exploring and modeling data, and how to automate infrastructure setup and deployment with tools like Packer to build machine images and Ansible for configuration management.
- The rise of DevOps and its cultural aspects are discussed as well as how tools like Packer, Ansible, Kubernetes can help automate infrastructure and deploy machine learning models at scale in production environments.
Big Data, NoSQL, NewSQL & The Future of Data ManagementTony Bain
It is an exciting and interesting time to be involved in data. More change of influence has occurred in the database management in the last 18 months than has occurred in the last 18 years. New technologies such as NoSQL & Hadoop and radical redesigns of existing technologies, like NewSQL , will change dramatically how we manage data moving forward.
These technologies bring with them possibilities both in terms of the scale of data retained but also in how this data can be utilized as an information asset. The ability to leverage Big Data to drive deep insights will become a key competitive advantage for many organisations in the future.
Join Tony Bain as he takes us through both the high level drivers for the changes in technology, how these are relevant to the enterprise and an overview of the possibilities a Big Data strategy can start to unlock.
My perspective on the evolution of big data from the perspective of a distributed systems researcher & engineer -- the background of how it get started, the scale-out paradigm, industry use cases, open source development paradigm, and interesting future challenges.
This document provides an introduction and overview of the INF2190 - Data Analytics course. It discusses the instructor, Attila Barta, details on where and when the course will take place. It then provides definitions and history of data analytics, discusses how the field has evolved with big data, and references enterprise data analytics architectures. It contrasts traditional vs. big data era data analytics approaches and tools. The objective of the course is described as providing students with the foundation to become data scientists.
The document discusses addressing data management challenges in the cloud. It begins by introducing the scale of digital data using common size prefixes like kilobyte and petabyte. It then discusses sources of massive data from sensors, social media, and scientific experiments. The challenges of big data are defined through the 3Vs model of increasing volume, velocity and variety of data types. Cloud computing architectures and delivery models like IaaS, PaaS and SaaS are introduced as ways to provide elastic resources for data management. The concept of polyglot persistence using the appropriate data store for the job is discussed over relying solely on relational databases.
This document provides an introduction to big data, including:
- Big data is characterized by its volume, velocity, and variety, which makes it difficult to process using traditional databases and requires new technologies.
- Technologies like Hadoop, MongoDB, and cloud platforms from Google and Amazon can provide scalable storage and processing of big data.
- Examples of how big data is used include analyzing social media and search data to gain insights, enabling personalized experiences and targeted advertising.
- As data volumes continue growing exponentially from sources like sensors, simulations, and digital media, new tools and approaches are needed to effectively analyze and make sense of "big data".
This document discusses big data, including opportunities and risks. It covers big data technologies, the big data market, opportunities and risks related to capital trends, and issues around algorithmic accountability and privacy. The document contains several sections that describe topics like the Internet of Things, Hadoop, analytics approaches for static versus streaming data, big data challenges, and deep learning. It also includes examples of big data use cases and discusses hype cycles, adoption curves, and strategies for big data adoption.
This document discusses steps towards a data value chain, including big data, public open data, and linked (open) data. It provides definitions and examples for each topic. For big data, it discusses the large volumes of data being created and challenges in working with such data. For public open data, it outlines principles like completeness and ease of access. It also shows examples of apps using open government data. For linked open data, it discusses moving from a web of documents to a web of interconnected data through using URIs and typed links. It also shows the growth of the linked open data cloud over time.
Big Data and Data Science: The Technologies Shaping Our LivesRukshan Batuwita
Big Data and Data Science have become increasingly imperative areas in both industry and academia to the extent that every company wants to hire a Data Scientist and every university wants to start dedicated degree programs and centres of excellence in Data Science. Big Data and Data Science have led to technologies that have already shaped different aspects of our lives such as learning, working, travelling, purchasing, social relationships, entertainments, physical activities, medical treatments, etc. This talk will attempt to cover the landscape of some of the important topics in these exponentially growing areas of Data Science and Big Data including the state-of-the-art processes, commercial and open-source platforms, data processing and analytics algorithms (specially large scale Machine Learning), application areas in academia and industry, the best industry practices, business challenges and what it takes to become a Data Scientist.
This document discusses an introduction to data and big data. It defines data and differentiates it from information. It then discusses the volume, variety, and velocity characteristics of big data known as the 3Vs. The document contrasts big data with small data and notes the challenges of working with big data due to its scale. Finally, it briefly mentions some major players in big data like Google and Hadoop and lists some common tools used for big data.
Beyond Triplets: Hyper-Relational Knowledge Graph Embedding for Link PredictioneXascale Infolab
1) The document presents HINGE, a new method for embedding hyper-relational knowledge graphs that aims to better capture information from facts containing multiple relations and entities.
2) HINGE uses a CNN to learn representations from base triplets and their associated key-value pairs to characterize the plausibility of facts.
3) An evaluation on link prediction tasks shows HINGE outperforms baselines and demonstrates that the triplet structure encodes essential information, while other representations discard important information.
Representation Learning on Graphs with Complex Structures
Invited talk, Deep Learning for Graphs and Structured Data Embedding Workshop
WWW2019, San Francisco, May 13, 2019
A force directed approach for offline gps trajectory mapeXascale Infolab
SIGSPATIAL 2018 paper
A Force-Directed Approach for Offline GPS Trajectory Map Matching
Efstratios Rappos (University of Applied Sciences of Western Switzerland (HES-SO)),
Stephan Robert (University of Applied Sciences of Western Switzerland (HES-SO)),
Philippe Cudré-Mauroux (University of Fribourg)
HistoSketch: Fast Similarity-Preserving Sketching of Streaming Histograms wit...eXascale Infolab
This document proposes HistoSketch, a method for sketching streaming histograms that preserves similarity and adapts to concept drift. It works by:
1) Generating weighted samples from histograms such that the probability two sketches match equals histogram similarity.
2) Incrementally updating sketches using a weight decay factor to forget older data and adapt to drift over time.
3) Evaluating HistoSketch on classification tasks involving synthetic and real-world streaming data, finding it approximates histogram similarity well using small, fixed-size sketches while adapting rapidly to drift.
This document presents SwissLink, a high-precision context-free entity linking system. It extracts unambiguous surface forms (labels) from knowledge bases like DBpedia and Wikipedia to link entity mentions without context. It catalogs the surface forms, removes ambiguous ones using ratio and percentile methods, and performs fast string matching to link mentions. Evaluation on 30 Wikipedia articles shows the percentile-ratio method achieves over 95% precision and 45% recall, balancing precision and recall.
The document proposes a novel crowdsourcing system architecture and scheduling algorithm to address job starvation in multi-tenant crowd-powered systems. The architecture introduces HIT-Bundles to group heterogeneous tasks and control task serving. The Worker Conscious Fair Scheduling algorithm balances fairness and priority while minimizing worker context switching between tasks. Experiments on Amazon Mechanical Turk show the approach increases throughput over baseline schedulers and adapts to varying workforce levels and job priorities.
This document presents SANAPHOR, an ontology-based coreference resolution system that improves upon existing approaches by leveraging semantic information. It first links entities in document clusters to semantic types and ontologies. It then splits or merges clusters based on these semantic relationships. The system was evaluated on the CoNLL-2012 dataset, where it improved coreference resolution performance over the baseline Stanford system, particularly for noun clusters. By utilizing semantic knowledge, SANAPHOR demonstrates the benefits of enhancing syntactic coreference resolution with an additional semantic layer.
Efficient, Scalable, and Provenance-Aware Management of Linked DataeXascale Infolab
The proliferation of heterogeneous Linked Data on the Web requires data management systems to constantly improve their scalability and efficiency. Despite recent advances in distributed Linked Data management, efficiently processing large amounts of Linked Data in a scalable way is still very challenging. In spite of their seemingly simple data models, Linked Data actually encode rich and complex graphs mixing both instance and schema level data. At the same time, users are increasingly interested in investigating or visualizing large collections of online data by performing complex analytic queries. The heterogeneity of Linked Data on the Web also poses new challenges to database systems. The capacity to store, track, and query provenance data is becoming a pivotal feature of Linked Data Management Systems. In this thesis, we tackle issues revolving around processing queries on big, unstructured, and heterogeneous Linked Data graphs.
1) Entity-centric data management stores information at the entity level and integrates information by interlinking entities. This provides advantages over keyword-based and relational database approaches.
2) The XI Pipeline extracts mentions from text and performs named entity recognition, entity linking, and entity typing to associate entities with text.
3) Approaches like ZenCrowd and TRank leverage both algorithms and human computation through crowdsourcing to improve entity linking and fine-grained entity typing.
This document summarizes a presentation given at SSSW 2015 on making sense of semantic data. It discusses challenges in understanding semantic web data, including a "language gap" between semantic web languages like SPARQL and natural language. It presents an approach to bridging this gap through automatically verbalizing SPARQL queries in English. Evaluation results show this helps non-experts understand queries better and faster than the SPARQL format. It also discusses the "semantic gap" caused by mismatches between a question's semantics and a knowledge graph, and presents an approach using templates to generate SPARQL queries from natural language questions.
LDOW2015 - Uduvudu: a Graph-Aware and Adaptive UI Engine for Linked DataeXascale Infolab
Uduvudu exploits the semantic and structured nature of Linked Data to generate the best possible representation for a human based on a catalog of available Matchers and Templates. Matchers and Templates are designed that they can be build through an intuitive editor interface.
Executing Provenance-Enabled Queries over Web DataeXascale Infolab
The proliferation of heterogeneous Linked Data on the Web poses new challenges to database systems. In particular, because of this heterogeneity, the capacity to store, track, and query provenance data is becoming a pivotal feature of modern triple stores. In this paper, we tackle the problem of efficiently executing provenance-enabled queries over RDF data. We propose, implement and empirically evaluate five different query execution strategies for RDF queries that incorporate knowledge of provenance. The evaluation is conducted on Web Data obtained from two different Web crawls (The Billion Triple Challenge, and the Web Data Commons). Our evaluation shows that using an adaptive query materialization execution strategy performs best in our context. Interestingly, we find that because provenance is prevalent within Web Data and is highly selective, it can be used to improve query processing performance. This is a counterintuitive result as provenance is often associated with additional overhead.
Micro-task crowdsourcing is rapidly gaining popularity among research communities and businesses as a means to leverage Human Computation in their daily operations. Unlike any other service, a crowdsourcing platform is in fact a marketplace subject to human factors that affect its performance, both in terms of speed and quality. Indeed, such factors shape the dynamics of the crowdsourcing market. For example, a known behavior of such markets is that increasing the reward of a set of tasks would lead to faster results. However, it is still unclear how different dimensions interact with each other: reward, task type, market competition, requester reputation, etc.
In this paper, we adopt a data-driven approach to (A) perform a long-term analysis of a popular micro-task crowdsourcing platform and understand the evolution of its main actors (workers, requesters, and platform). (B) We leverage the main findings of our five year log analysis to propose features used in a predictive model aiming at determining the expected performance of any batch at a specific point in time. We show that the number of tasks left in a batch and how recent the batch is are two key features of the prediction. (C) Finally, we conduct an analysis of the demand (new tasks posted by the requesters) and supply (number of tasks completed by the workforce) and show how they affect task prices on the marketplace.
Fixing the Domain and Range of Properties in Linked Data by Context Disambigu...eXascale Infolab
This document proposes three methods - LEXT, REXT, and LERIXT - for disambiguating the domain and range of properties in linked data by using context information. LEXT uses the type of subject resources, REXT uses the type of object resources, and LERIXT uses both. The methods were evaluated against expert judgments and achieved up to 96.5% precision for LEXT and 91.4% for REXT. LERIXT generated too many new sub-properties.
CIKM14: Fixing grammatical errors by preposition rankingeXascale Infolab
The detection and correction of grammatical errors still represent very hard problems for modern error-correction systems. As an example, the top-performing systems at the preposition correction challenge CoNLL-2013 only achieved a F1 score of 17%.
In this paper, we propose and extensively evaluate a series of approaches for correcting prepositions, analyzing a large body of high-quality textual content to capture language usage. Leveraging n-gram statistics, association measures, and machine learning techniques, our system is able to learn which words or phrases govern the usage of a specific preposition. Our approach makes heavy use of n-gram statistics generated from very large textual corpora. In particular, one of our key features is the use of n-gram association measures (e.g., Pointwise Mutual Information) between words and prepositions to generate better aggregated preposition rankings for the individual n-grams.
We evaluate the effectiveness of our approach using cross-validation with different feature combinations and on two test collections created from a set of English language exams and StackExchange forums. We also compare against state-of-the-art supervised methods. Experimental results from the CoNLL-2013 test collection show that our approach to preposition correction achieves ~30% in F1 score which results in 13% absolute improvement over the best performing approach at that challenge.
OLTPBenchmark is a multi-threaded load generator. The framework is designed to be able to produce variable rate, variable mixture load against any JDBC-enabled relational database. The framework also provides data collection features, e.g., per-transaction-type latency and throughput logs.
Together with the framework we provide the following OLTP/Web benchmarks:
TPC-C
Wikipedia
Synthetic Resource Stresser
Twitter
Epinions.com
TATP
AuctionMark
SEATS
YCSB
JPAB (Hibernate)
CH-benCHmark
Voter (Japanese "American Idol")
SIBench (Snapshot Isolation)
SmallBank
LinkBench
CH-benCHmark
This document discusses a project called MEM0R1ES that aims to automatically organize a person's digital information from various devices and online services to generate useful digital memories. The project develops techniques for entity search, typing, clustering, and elicitation to extract, integrate and expose personal information from heterogeneous graphs. It has produced several open-source software components and published results in top conferences. The document outlines current research directions and concludes that the project addresses important societal issues through stimulating collaboration between institutions.
Predictably Improve Your B2B Tech Company's Performance by Leveraging DataKiwi Creative
Harness the power of AI-backed reports, benchmarking and data analysis to predict trends and detect anomalies in your marketing efforts.
Peter Caputa, CEO at Databox, reveals how you can discover the strategies and tools to increase your growth rate (and margins!).
From metrics to track to data habits to pick up, enhance your reporting for powerful insights to improve your B2B tech company's marketing.
- - -
This is the webinar recording from the June 2024 HubSpot User Group (HUG) for B2B Technology USA.
Watch the video recording at https://youtu.be/5vjwGfPN9lw
Sign up for future HUG events at https://events.hubspot.com/b2b-technology-usa/
Open Source Contributions to Postgres: The Basics POSETTE 2024ElizabethGarrettChri
Postgres is the most advanced open-source database in the world and it's supported by a community, not a single company. So how does this work? How does code actually get into Postgres? I recently had a patch submitted and committed and I want to share what I learned in that process. I’ll give you an overview of Postgres versions and how the underlying project codebase functions. I’ll also show you the process for submitting a patch and getting that tested and committed.
End-to-end pipeline agility - Berlin Buzzwords 2024Lars Albertsson
We describe how we achieve high change agility in data engineering by eliminating the fear of breaking downstream data pipelines through end-to-end pipeline testing, and by using schema metaprogramming to safely eliminate boilerplate involved in changes that affect whole pipelines.
A quick poll on agility in changing pipelines from end to end indicated a huge span in capabilities. For the question "How long time does it take for all downstream pipelines to be adapted to an upstream change," the median response was 6 months, but some respondents could do it in less than a day. When quantitative data engineering differences between the best and worst are measured, the span is often 100x-1000x, sometimes even more.
A long time ago, we suffered at Spotify from fear of changing pipelines due to not knowing what the impact might be downstream. We made plans for a technical solution to test pipelines end-to-end to mitigate that fear, but the effort failed for cultural reasons. We eventually solved this challenge, but in a different context. In this presentation we will describe how we test full pipelines effectively by manipulating workflow orchestration, which enables us to make changes in pipelines without fear of breaking downstream.
Making schema changes that affect many jobs also involves a lot of toil and boilerplate. Using schema-on-read mitigates some of it, but has drawbacks since it makes it more difficult to detect errors early. We will describe how we have rejected this tradeoff by applying schema metaprogramming, eliminating boilerplate but keeping the protection of static typing, thereby further improving agility to quickly modify data pipelines without fear.
4th Modern Marketing Reckoner by MMA Global India & Group M: 60+ experts on W...Social Samosa
The Modern Marketing Reckoner (MMR) is a comprehensive resource packed with POVs from 60+ industry leaders on how AI is transforming the 4 key pillars of marketing – product, place, price and promotions.
Orchestrating the Future: Navigating Today's Data Workflow Challenges with Ai...Kaxil Naik
Navigating today's data landscape isn't just about managing workflows; it's about strategically propelling your business forward. Apache Airflow has stood out as the benchmark in this arena, driving data orchestration forward since its early days. As we dive into the complexities of our current data-rich environment, where the sheer volume of information and its timely, accurate processing are crucial for AI and ML applications, the role of Airflow has never been more critical.
In my journey as the Senior Engineering Director and a pivotal member of Apache Airflow's Project Management Committee (PMC), I've witnessed Airflow transform data handling, making agility and insight the norm in an ever-evolving digital space. At Astronomer, our collaboration with leading AI & ML teams worldwide has not only tested but also proven Airflow's mettle in delivering data reliably and efficiently—data that now powers not just insights but core business functions.
This session is a deep dive into the essence of Airflow's success. We'll trace its evolution from a budding project to the backbone of data orchestration it is today, constantly adapting to meet the next wave of data challenges, including those brought on by Generative AI. It's this forward-thinking adaptability that keeps Airflow at the forefront of innovation, ready for whatever comes next.
The ever-growing demands of AI and ML applications have ushered in an era where sophisticated data management isn't a luxury—it's a necessity. Airflow's innate flexibility and scalability are what makes it indispensable in managing the intricate workflows of today, especially those involving Large Language Models (LLMs).
This talk isn't just a rundown of Airflow's features; it's about harnessing these capabilities to turn your data workflows into a strategic asset. Together, we'll explore how Airflow remains at the cutting edge of data orchestration, ensuring your organization is not just keeping pace but setting the pace in a data-driven future.
Session in https://budapestdata.hu/2024/04/kaxil-naik-astronomer-io/ | https://dataml24.sessionize.com/session/667627
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...sameer shah
"Join us for STATATHON, a dynamic 2-day event dedicated to exploring statistical knowledge and its real-world applications. From theory to practice, participants engage in intensive learning sessions, workshops, and challenges, fostering a deeper understanding of statistical methodologies and their significance in various fields."
Codeless Generative AI Pipelines
(GenAI with Milvus)
https://ml.dssconf.pl/user.html#!/lecture/DSSML24-041a/rate
Discover the potential of real-time streaming in the context of GenAI as we delve into the intricacies of Apache NiFi and its capabilities. Learn how this tool can significantly simplify the data engineering workflow for GenAI applications, allowing you to focus on the creative aspects rather than the technical complexities. I will guide you through practical examples and use cases, showing the impact of automation on prompt building. From data ingestion to transformation and delivery, witness how Apache NiFi streamlines the entire pipeline, ensuring a smooth and hassle-free experience.
Timothy Spann
https://www.youtube.com/@FLaNK-Stack
https://medium.com/@tspann
https://www.datainmotion.dev/
milvus, unstructured data, vector database, zilliz, cloud, vectors, python, deep learning, generative ai, genai, nifi, kafka, flink, streaming, iot, edge
Internet Infrastructures for Big Data (Verisign's Distinguished Speaker Series)
1. Internet Infrastructures
for Big Data
Philippe Cudré-Mauroux
eXascale Infolab, University of Fribourg
Switzerland
VeriSign EMEA
June 26, 2014
1
2. eXascale Infolab
• New lab @ U. of Fribourg, Switzerland
• Financed by Swiss Federal State / companies / private
foundations
• Big (non-relational) data management
(Volume, Velocity, Variety) (… mostly)
2
3. On the Menu Today
• Big Data!
– Big Data Buzz
– 3 Big Data projects w/ XI & Verisign
3
5. Big Data “Central Theorem”
Data+Technology Actionable Insight $$
Reporting, Monitoring, Root Cause Analysis,
(User) Modelization, Prediction
5
6. Big Data Buzz
6
Between now and 2015, the firm expects big data to
create some 4.4 million IT jobs globally; of those, 1.9
million will be in the U.S. Applying an economic
multiplier to that estimate, Gartner expects each new big-
data-related IT job to create work for three more people
outside the tech industry, for a total of almost 6 million
more U.S. jobs.
Growth in the Asia Pacific Big Data market
is expected to accelerate rapidly in two to
three years time, from a mere US$258.5
million last year to in excess of $1.76
billion in 2016, with highest growth in the
storage segment.
7. Big Data Everywhere!
• The Age of Big Data (NYTimes Feb. 11, 2012)
http://www.nytimes.com/2012/02/12/sunday-review/big-datas-impact-in-
the-world.html
“Welcome to the Age of Big Data. The new megarich of Silicon Valley,
first at Google and now Facebook, are masters at harnessing the data
of the Web — online searches, posts and messages — with Internet
advertising. At the World Economic Forum last month in Davos,
Switzerland, Big Data was a marquee topic. A report by the forum, “Big
Data, Big Impact,” declared data a new class of economic asset, like
currency or gold.”
7
10. The 3-Vs of Big Data
• Volume
– amount of data
• Velocity
– speed of data in and out
• Variety
– range of data types and sources
• [Gartner 2012] "Big Data are high-volume, high-velocity, and/or
high-variety information assets that require new forms of
processing to enable enhanced decision making, insight
discovery and process optimization"
Coming up: 3
examples from XI
10
11. Volume: Fixing the Hadoop
Distributed File System
• Hadoop (YARN): “cluster Operating System”
• Often synonymous with Big Data
• Used everywhere (… even in CH)
11
12. HDFS Blocks Placement Strategy
Rack 1 Rack 2
● 1st replica on local
node or random
node
● 2nd replica on a
different node in a
different rack
● 3rd replica on a
different node in
same rack as 2nd
replica
➡Not hardware-aware
➡Block level rather than file level
13. Solution: Hadaps File Placement
• Assigns weights to DataNodes
– I/O-bound jobs finish earlier on new media
– CPU-bound jobs finish earlier on new CPUs
• Uses lower utilization servers first
• Moves more blocks to newer generations
• Operates on file level
Up to 300% performance
improvement by activating
all nodes
1
A
1
2
B
1
2
C
1
2
D
2
3
E
2
3
F
2
3
2
34
56
7
8
9
Blocks
Weight
123456
789
1 2
3
4
5
6
7 8
9
10
10
10
16. Data at each Vertex!
• Spatial + temporal statistical processing (mini-
Lisas)
• Stream processing (Storm) + Array processing
(SciDB)
base
station 29
sensor 1053
sensor 1054
base
station 17
base
station 42Peer Information Management overlay
Array Data Management System
OLTP HYRISE OLAP
OLTP HYRISE OLAP
OLTP HYRISE OLAP
Anomaly
Detection
Alert
Sliding-Window
Average
Data Gap
Event
Mini-Lisa
Computations
Missing Data?
Anomaly
Detected?
Yes
No
Yes Anomaly
Event
Delta
Compression
Fluctuation?
Yes Publish
Value
Event
No
No
Alive Event
Stream Processing Flow
16
18. Variety: Sharing Data Locally & Globally
• 70+% of the world’s population has no or
very limited access to the Web
[Ahmed Shams 2013]
18
19. Our Solution: ERS, the
Entity Registry System
• Three-tier solution to deploy data-powered apps
– Flexible
• Seamlessly reconcile entities in local / ad-hoc / global modes
– Collaborative
• Transactional consistency,
data versioning
– Scalable
• Bridges, scale-out servers,
tunable consistency
– Open-source
• https://github.com/ers-devs
19
20. Ongoing Deployments
• Entity-powered apps for the Sugar Learning
Platform
• Ambient Assisted Living of elderly persons
in tropical environments
20
21. Special Thanks to…
• Vincenzo Russo, Benoit Perroud, Matt
Thomas, Romain Cholat and the whole
Verisign Fribourg office
• Burt Kaliski and his team
• Allison Mankin, Scott Hollenbeck, Debra
Anderson & the Internet Infrastructures Grant
team
… for their continued support